Document Type
Article
Publication Date
5-28-2024
Abstract
Abstract: Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration.
Language
English
Publication Title
MRS Advances
Grant
DE-NA0004104
Rights
© The Author(s) 2024. This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hernandez, K.J., Barcelos, E.I., Jimenez, J.C. et al. A data integration framework of additive manufacturing based on FAIR principles. MRS Advances 9, 844–851 (2024). https://doi.org/10.1557/s43580-024-00874-5
Manuscript Version
Final Publisher Version